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Need to retrieve rows from table where the following condition was not satisfied column A = column B and column B = colu


Need to retrieve rows from table where the following condition was not satisfied column A = column B and column B = colu

By : Stephen Langdon
Date : November 22 2020, 10:33 AM
hop of those help? I hope I am able to explain what I am looking for. I have a table as follows: , The following should work:
code :
SELECT * FROM TABLE1 T1 
WHERE NOT EXISTS (SELECT 1 FROM TABLE1 T2 
                  WHERE T1.ColumnA = T2.ColumnB AND 
                        T1.ColumnB = T2.ColumnA)


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Average of Numbers in Column If Condition In Different Column Satisfied

Average of Numbers in Column If Condition In Different Column Satisfied


By : Alexandre
Date : March 29 2020, 07:55 AM
help you fix your problem There is a lot more simple version. Just put into the Destination Cell the formula
=ArrayFormula(AVERAGE(IF(DATEDIF(C1:C;TODAY();"D")<=90;E1:E)))
R: Assign values to a new column based on values of another column where a condition is satisfied

R: Assign values to a new column based on values of another column where a condition is satisfied


By : Brian Walker
Date : March 29 2020, 07:55 AM
Does that help without an example its quite hard to tell. But from your description it sounds like a base::merge or dplyr::inner_join operation. Those are quite fast in comparison to if statements.
Cheers
group a column by values of second column based on condition from third column in data.table

group a column by values of second column based on condition from third column in data.table


By : Îslam N Clancy's
Date : March 29 2020, 07:55 AM
I wish this help you After grouping by 'Group', take the absolute difference of 'Col2' with 0.5, get the index o the minimum value (which.min) and use that to subset the 'Col1'
code :
DAT[, .(Col1 = Col1[which.min(abs(Col2 - 0.5))]), Group]
#   Group Col1
#1:     A    4
#2:     B    8
How to create a new column where it takes value of another column when another column's condition is satisfied

How to create a new column where it takes value of another column when another column's condition is satisfied


By : user1543077
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Another way is set all values to 0 and then change the satisfied ones :
code :
df[3] <- c(0,nrow(df))
D[D[,2]==1,3]=D[D[,2]==1,1]
Adding a column to a PySpark dataframe contans standard deviations of a column based on the grouping on two another colu

Adding a column to a PySpark dataframe contans standard deviations of a column based on the grouping on two another colu


By : user2312178
Date : March 29 2020, 07:55 AM
hop of those help? The join on dataframe causes a lot of data shuffle between executors. In your case, you can do without the join. Use a window specification to partition data by 'lable' and 'year' and aggregate on the window.
code :
from pyspark.sql.window import *

windowSpec = Window.partitionBy('lable','year')\
                   .rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)

df = df.withColumn("std", f.round(f.stddev("val").over(windowSpec), 2))
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